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Multitask Pointer Network for Multi-Representational Parsing
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Abstract:
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG ; [Abstract] Dependency and constituent trees are widely used by many artificial intelligence applications for representing the syntactic structure of human languages. Typically, these structures are separately produced by either dependency or constituent parsers. In this article, we propose a transition-based approach that, by training a single model, can efficiently parse any input sentence with both constituent and dependency trees, supporting both continuous/projective and discontinuous/non-projective syntactic structures. To that end, we develop a Pointer Network architecture with two separate task-specific decoders and a common encoder, and follow a multitask learning strategy to jointly train them. The resulting quadratic system, not only becomes the first parser that can jointly produce both unrestricted constituent and dependency trees from a single model, but also proves that both syntactic formalisms can benefit from each other during training, achieving state-of-the-art accuracies in several widely-used benchmarks such as the continuous English and Chinese Penn Treebanks, as well as the discontinuous German NEGRA and TIGER datasets. ; We acknowledge the European Research Council (ERC), which has funded this research under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), ERDF/MICINN-AEI (ANSWER-ASAP, TIN2017-85160-C2-1-R; SCANNER-UDC, PID2020-113230RB-C21), Xunta de Galicia, Spain (ED431C 2020/11), and Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia, Spain and the European Union (ERDF - Galicia 2014–2020 Program), by grant ED431G 2019/01. Funding for open access charge: Universidade da Coruña / CISUG ; Xunta de Galicia; ED431C 2020/11 ; Xunta de Galicia; ED431G 2019/01
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Keyword:
Computational linguistics; Constituent parsing; Deep learning; Dependency parsing; Natural language processing; Neural network; Parsing
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URL: https://doi.org/10.1016/j.knosys.2021.107760
http://hdl.handle.net/2183/29887
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| 62 |
Horse or pony? Visual Typicality and Lexical Frequency Affect Variability in Object Naming
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 63 |
Masked language models directly encode linguistic uncertainty
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 64 |
Learning Stress Patterns with a Sequence-to-Sequence Neural Network
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 65 |
Modeling human-like morphological prediction
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 66 |
The interaction between cognitive ease and informativeness shapes the lexicons of natural languages
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 67 |
What is so Plautine about Plautine Language? Computers and the Style of Early Latin Drama
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In: Peter Barrios-Lech (2022)
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| 68 |
How Well Do LSTM Language Models Learn Filler-gap Dependencies?
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 69 |
A split-gesture, competitive, coupled oscillator model of syllable structure predicts the emergence of edge gemination and degemination
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 70 |
Linguistic Complexity and Planning Effects on Word Duration in Hindi Read Aloud Speech
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 71 |
Learning Argument Structures with Recurrent Neural Network Grammars
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 72 |
MaxEnt Learners are Biased Against Giving Probability to Harmonically Bounded Candidates
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 73 |
Evaluating Structural Economy Claims in Relative Clause Attachment
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 74 |
A Model Theoretic Perspective on Phonological Feature Systems
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 75 |
Representing Multiple Dependencies in Prosodic Structures
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 76 |
Incremental Acquisition of a Minimalist Grammar using an SMT-Solver
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 77 |
Concurrent hidden structure & grammar learning
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 78 |
Can language models capture syntactic associations without surface cues? A case study of reflexive anaphor licensing in English control constructions
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 79 |
Universal Dependencies and Semantics for English and Hebrew Child-directed Speech
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 80 |
Learning Input Strictly Local Functions: Comparing Approaches with Catalan Adjectives
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In: Proceedings of the Society for Computation in Linguistics (2022)
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